Abstract: A system, method and computer-readable medium are disclosed for identifying representative data using sketches. The method embodiment comprises generating a plurality of vectors from a data set, modifying each of the vectors of the plurality of vectors and selecting one of the plurality of generated vectors according to a comparison of a summed distance between a modified vector associated with the selected generated vector and remaining modified vectors. Modifying the generated vectors may involve reduced each generated vector to a lower dimensional vector. The summed distance then represents a summed distance between the lower dimensional vector and remaining lower dimensional vectors.

Abstract: Methods, systems, devices, and computer program products for decoding of Walsh codewords are disclosed.

Abstract: A system and method are provided for monitoring dynamic data from distributed sources through the use of histograms. In the method, an array sketch of the digital signal is determined, a robust histogram is constructed from the array sketch, and an output histogram is constructed from the array sketch and the robust histogram via a hybrid histogram. Dyadic intervals of a representation of the array sketch are used in constructing the robust histogram.

Abstract: A method and system for identifying representative data trends using sketches. A sketch is a lower dimensional vector used to represent higher dimensional data. The properties of sketches include data dimensionality reduction, sketches synthesized from other sketches, and the distance between sketches comparable to the distance between the data the sketches represent. Exemplary embodiments include identifying relaxed periods and average trends.

Abstract: A method, apparatus, and article of manufacture for a computer implemented hypertext classifier. A new document containing citations to and from other documents is classified. Initially, documents within a neighborhood of the new document are identified. For each document and each class, an initial probability is determined that indicates the probability that the document fits a particular class. Next, iterative relaxation is performed to identify a class for each document using the initial probabilities. A class is selected into which the new document is to be classified based on the initial probabilities and identified classes.